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--- |
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base_model: google/gemma-2b |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- sentiment-analysis |
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- lora |
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- transformers |
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- peft |
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--- |
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# Sentiment Analyzer |
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A fine-tuned sentiment analysis model developed and shared by **Pavithrapn-01**. |
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This model is designed to analyze text and classify sentiment efficiently using a lightweight fine-tuning approach. |
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--- |
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## Model Details |
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### Model Description |
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This model is a **sentiment analysis system** built by fine-tuning the **google/gemma-2b** base model using **LoRA (Low-Rank Adaptation)**. |
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It is optimized for understanding emotional polarity in text such as **positive, negative, or neutral sentiment**. |
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- **Developed by:** Pavithra PN |
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- **Shared by:** Pavithrapn-01 |
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- **Model type:** Text Generation / Sentiment Analysis |
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- **Language(s):** English |
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- **License:** Open-source (same as base model) |
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- **Finetuned from model:** google/gemma-2b |
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--- |
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## Model Sources |
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- **Repository:** Pavithrapn-01/sentiment-analyzer |
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- **Base Model:** google/gemma-2b |
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--- |
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## Uses |
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### Direct Use |
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- Sentiment analysis of user reviews |
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- Opinion mining from social media text |
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- Feedback and survey analysis |
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- Educational and academic projects |
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### Downstream Use |
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- Can be integrated into chatbots |
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- Can be used in recommendation systems |
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- Can be further fine-tuned for domain-specific sentiment tasks |
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### Out-of-Scope Use |
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- Medical or legal decision-making |
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- High-risk or safety-critical applications |
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- Multilingual sentiment analysis (English only) |
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--- |
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## Bias, Risks, and Limitations |
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- The model may reflect biases present in the training data |
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- Performance may vary on slang, sarcasm, or ambiguous text |
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- Best suited for short to medium-length text inputs |
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### Recommendations |
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Users should validate outputs before deploying the model in real-world applications and avoid using it for sensitive decision-making. |
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--- |
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## How to Get Started with the Model |
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```python |
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from transformers import pipeline |
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classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer") |
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result = classifier("I really enjoyed using this application!") |
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print(result) |
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